Evaluation of adaptive algorithms for detection and classification of fluorescent aerosols in the atmosphere

P. Lahaie, J. Simard, S. Buteau
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引用次数: 4

Abstract

Photon counting technologies are developed and could be used in the future to measure the return from laser induced fluorescence. Currently, the spectral detection of light emitted by fluorescing aerosols is performed with ICCD, Intensified Charge Coupled Device. The signal to noise ratio of ICCD devices is smaller by a factor of √2compared to photon counting devices having the same sensitivity. We studied the impact of this difference of signal to noise ratio on the capability of multivariate detection and classification algorithms to operate on various conditions. Signal simulations have been performed to obtain ROC (Receiver Operation Characteristics) Curves and Confusion Matrix to obtain the detection performance and the ability of algorithms to discriminate a potential source from another. Two detection algorithms are used, the Integrated Laser Induced Fluorescence(ILIF) and the Matched Filter. For the classification, three algorithms are used, the Adaptive Matched Filter (AMF), the Adaptive Coherent Estimator (ACE) and the Adaptive Least Squares (ALS). The best algorithm for detection is the AMF using the signature of the material present in a cloud, the ILIF detector performs very well. For the classification, the three algorithms are surprisingly giving the same results for the same data. The classification performs better if the distance between the signatures recorded in a database is important. The performance of the detector and of the classificator improves with an increase of the signal to noise ratio and is consistently and significantly better for the photon counting compared to ICCD.
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大气中荧光气溶胶检测和分类的自适应算法评价
光子计数技术的发展,可用于测量激光诱导荧光的返回。目前,荧光气溶胶发射光的光谱检测是用ICCD(强化电荷耦合器件)进行的。与具有相同灵敏度的光子计数器件相比,ICCD器件的信噪比要小√2倍。我们研究了这种信噪比的差异对多元检测和分类算法在不同条件下运行能力的影响。进行了信号仿真以获得ROC (Receiver Operation Characteristics)曲线和混淆矩阵,以获得检测性能和算法区分潜在源的能力。采用了集成激光诱导荧光(ILIF)和匹配滤波器两种检测算法。在分类方面,采用了自适应匹配滤波(AMF)、自适应相干估计(ACE)和自适应最小二乘(ALS)三种算法。检测的最佳算法是利用云中存在的材料的特征的AMF, ILIF检测器表现非常好。对于分类,这三种算法对相同的数据给出了令人惊讶的相同结果。如果数据库中记录的签名之间的距离很重要,则分类效果会更好。检测器和分类器的性能随着信噪比的增加而提高,并且与ICCD相比,在光子计数方面表现出一致性和显著性的改善。
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